Over the past two years the term “foundation model” has moved from research labs into enterprise boardrooms. By November 2024 many organizations have evaluated large language models (LLMs) and multi-modal models, but few have truly operationalized them at scale. In this post we outline practical steps for turning experimentation into enterprise-grade deployments.
Defining the objective and scope
Before selecting a model, ask: what value will this model deliver, and how will it integrate into existing workflows?
- Identify a clear, measurable business outcome (for example: reduce call-center average handle time, or automate contract review).
- Specify the scope of inputs and outputs — are you dealing only with unstructured text, or combining text and image?
- Determine whether a pre-trained foundation model can be used directly, or needs fine-tuning or retrieval-based augmentation.
Architecture patterns for production readiness
When moving into production, you’ll want to adopt robust architecture patterns. Some common patterns:
- Model-as-service: Deploy the foundation model behind an internal API gateway, enabling consistent access and rate-limiting.
- Hybrid embedding + retrieval: For domain-specific knowledge, embed your proprietary data and use retrieval-augmented generation rather than fine-tuning a whole model.
- Parallel inference fallback: Run the foundation model in parallel with a legacy deterministic system; monitor divergence and fallback to the legacy path if confidence is low.
- Edge or on-prem deployments: In regulated industries or low-latency contexts, deploy a compressed version of the model locally or on specialized hardware.
Governance, risk and compliance (GRC) frameworks
Model governance remains a critical barrier to enterprise adoption. Key considerations include:
- Bias and fairness: Conduct bias audits before deployment, address underperforming cohorts, and monitor drift over time.
- Explainability and traceability: Log model inputs, outputs, and confidence scores; maintain version control over model artifacts and data pipelines.
- Security and privacy: Ensure that the model does not inadvertently leak sensitive information from training data; enforce differential privacy or data-masking where required.
- Accountability and human-in-the-loop (HITL): Define clear roles for model owners, operators and auditors; maintain HITL overrides in critical decisions.
Data strategy: aligning data and model lifecycle
High-quality, well-managed data is essential for foundation models to deliver on their promise. Consider the following:
- Data-catalogue integration: Link inputs and outputs to enterprise data catalogues, enabling lineage and impact analysis.
- Continuous data refresh: Establish pipelines to incorporate new and changing data into embeddings or fine-tuning sets, rather than relying on one-time static snapshots.
- Feedback loops and annotation pipelines: Build annotation workflows for when models make errors, and feed those back into retraining or model-adjustment cycles.
- Domain-specific embeddings: Use proprietary data (legal contracts, engineering drawings, customer transcripts) to generate embeddings that sit alongside the foundation model.
Monitoring, metrics and continuous improvement
To maintain operational reliability, you must monitor not just system health but model performance:
- Latency and throughput: Track inference time, queue lengths, error rates.
- Outcome metrics: Tie model usage to business KPIs (for instance: reduction in human intervention rate, improvement in resolution accuracy).
- Concept drift detection: Monitor input distributions and output patterns for divergence from training distribution; consider automated retraining triggers.
- Cost monitoring: Foundation models can incur large compute and storage costs; track cost per successful outcome or per inference and optimize accordingly.
Cultural and organizational readiness
Technical solutions alone won’t succeed without organizational alignment:
- Executive sponsorship: Ensure leadership understands the risks and value of foundation-model deployments.
- Cross-functional teams: Bring together data science, engineering, compliance, and business functions early in the deployment lifecycle.
- Training and change management: Equip users with knowledge of how to interact with the model, when to escalate to HITL, and how to interpret outputs.
Moving from pilot to scale
Most organizations are still at the pilot or sandbox stage. To scale effectively:
- Standardize tooling and pipelines so each new model reuse infrastructure, monitoring and governance frameworks.
- Modularise model deployments so you can swap in newer or more efficient models without rewriting all downstream logic.
- Develop a model-registry and versioning discipline—a foundation model with fine-tuned variants should be tracked just like any other software release.
- Audit and retire legacy models to avoid model sprawl, redundant technical debt and unmanaged risk.
Conclusion
The arrival of foundation models opens new possibilities, but real-world deployment demands more than access to an API. What differentiates leaders is maturity in architecture, governance, data management, monitoring and organizational readiness. As of November 2024, companies that treat foundation model deployment as an engineering and operational discipline—not just a research experiment—are gaining a real competitive advantage.
If your organization is evaluating foundation models and wants to assess readiness, the key question is: are you ready to operate them as live systems, or are you still experimenting? Addressing this gap is where value gets unlocked.